Image to Perturbation: An Image Transformation Network for Generating Visually Protected Images for Privacy-Preserving Deep Neural Networks

Abstract

We propose a novel image transformation network for generating visually protected images for privacy-preserving deep neural networks (DNNs). The proposed transformation network is trained by using a plain image dataset so that plain images are converted into visually protected ones. Conventional perceptual encryption methods cause some accuracy degradation in image classification and are not robust enough against state-of-the-art attacks. In contrast, the proposed network not only enables us to maintain the image classification accuracy that using plain images achieves but is also strongly robust against attacks including DNN-based ones. Furthermore, there is no need to manage any security keys as the conventional methods require. In an image classification experiment, the proposed network is demonstrated to strongly protect the visual information of plain images while maintaining a high classification accuracy under the use of two typical classification networks: ResNet and VGG. In addition, it is shown that the visually protected images are robust enough against various attacks in an experiment in which we tried to restore the visual information of plain images.

Keywords

• Adversarial example
• deep neural network
• privacy preserving
• visual protection